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Correntropy: Properties and Applications in Non-Gaussian Signal Processing

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3 Author(s)
Weifeng Liu ; Florida Univ., Gainesville ; Pokharel, P.P. ; Principe, J.C.

The optimality of second-order statistics depends heavily on the assumption of Gaussianity. In this paper, we elucidate further the probabilistic and geometric meaning of the recently defined correntropy function as a localized similarity measure. A close relationship between correntropy and M-estimation is established. Connections and differences between correntropy and kernel methods are presented. As such correntropy has vastly different properties compared with second-order statistics that can be very useful in non-Gaussian signal processing, especially in the impulsive noise environment. Examples are presented to illustrate the technique.

Published in:

Signal Processing, IEEE Transactions on  (Volume:55 ,  Issue: 11 )

Date of Publication:

Nov. 2007

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